People have been using transfer learning in computer vision (CV) for a considerable time now, and it has produced remarkable results in these few years. In some tasks, we have been able to surpass human level accuracy as well. These days, implementations that don’t use pretrained weights to produce state-of-the-art results are rare. In fact, when people do produce them, it’s often understood that transfer learning or some sort of fine-tuning is being used. Transfer learning has had a huge impact in the field of computer vision and has contributed progressively in advancement of this field. Transfer Learning was kind of limited to computer vision up till now, but recent research work shows that the impact can be extended almost everywhere, including natural language processing (NLP), reinforcement learning (RL). Recently, a few papers have been published that show that transfer learning and fine-tuning work in NLP as well and the results are great. Recently OpenAI also had a retro contest in which participants were challenged to create agents that can play games without having access to the environment which was used to train it using transfer learning. It's now possible to explore the potential of this method.

An article from Bloomberg on Bonjour-Santé, a Montréal-based company that has developed an appointment service that can reduce the waiting time in medical clinics to 20 minutes using deep learning models. The company has received support from MILA, Yoshua Bengio's lab at Université de Montréal.

A group of tech companies (AMD, Baidu, Google, Intel, and others) and universities (Harvard, Berkeley, Minnesota, Stanford, Toronto) released a new benchmarking tool for machine learning software and hardware called MLPerf.

On the social issues... In the MIT Technology Review, Andrew Ng advocates for a “New Deal” (inspired by the government's programs implemented during the 1930s Great Depression) to combat next job automation wave caused by artificial intelligence.

Geoffrey Hinton’s paper on capsules has been released. I thought I saw the announcement in this G+ community, but maybe I've dreamt, maybe the post was removed. Anyway, here and now, the eagerly awaited paper... The abstract: https://goo.gl/17SbHv, the full paper (PDF): https://goo.gl/uKAu7b